Solar Wind Classification Via k -Means Clustering Algorithm

Heidrich-Meisner, Verena; Wimmer‐Schweingruber, R. F.

doi:10.1016/b978-0-12-811788-0.00016-0

Cited by 19 publications

(11 citation statements)

References 31 publications

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“…Types of solar wind can be defined depending on the charge state composition of the solar wind, solar wind speed, proton temperature, proton density, etc. (for example, as done in Heidrich‐Meisner & Wimmer‐Schweingruber, or Xu & Borovsky, ).…”

Section: Discussionmentioning

confidence: 99%

Systematic Analysis of Machine Learning and Feature Selection Techniques for Prediction of the Kp Index

et al. 2019

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The Kp index is a measure of the midlatitude global geomagnetic activity and represents short‐term magnetic variations driven by solar wind plasma and interplanetary magnetic field. The Kp index is one of the most widely used indicators for space weather alerts and serves as input to various models, such as for the thermosphere and the radiation belts. It is therefore crucial to predict the Kp index accurately. Previous work in this area has mostly employed artificial neural networks to nowcast Kp, based their inferences on the recent history of Kp and on solar wind measurements at L1. In this study, we systematically test how different machine learning techniques perform on the task of nowcasting and forecasting Kp for prediction horizons of up to 12 hr. Additionally, we investigate different methods of machine learning and information theory for selecting the optimal inputs to a predictive model. We illustrate how these methods can be applied to select the most important inputs to a predictive model of Kp and to significantly reduce input dimensionality. We compare our best performing models based on a reduced set of optimal inputs with the existing models of Kp, using different test intervals, and show how this selection can affect model performance.

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Section: Discussionmentioning

confidence: 99%

Systematic Analysis of Machine Learning and Feature Selection Techniques for Prediction of the Kp Index

et al. 2019

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“…In Space Weather, an unsupervised classification of the solar wind has been performed in Heidrich‐Meisner and Wimmer‐Schweingruber (), and a self‐organizing map has been applied to radiation belt particle distributions in Souza et al (). It is fair to say, however, that the majority of past studies have focused on supervised learning.…”

Section: Machine Learning In Space Weathermentioning

confidence: 99%

The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

2019

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The numerous recent breakthroughs in machine learning make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in Space Weather. The purpose is twofold. On one hand, we will discuss previous works that use machine learning for Space Weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the Space Weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray box. Plain Language SummaryIn the last decade, machine learning has achieved unforeseen results in industrial applications. In particular, the combination of massive data sets and computing with specialized processors (graphics processing units, or GPUs) can perform as well or better than humans in tasks like image classification and game playing. Space weather is a discipline that lives between academia and industry, given the relevant physical effects on satellites and power grids in a variety of applications, and the field therefore stands to benefit from the advances made in industrial applications. Today, machine learning poses both a challenge and an opportunity for the space weather community. The challenge is that the current data science revolution has not been fully embraced, possibly because space physicists remain skeptical of the gains achievable with machine learning. If the community can master the relevant technical skills, they should be able to appreciate what is possible within a few years time and what is possible within a decade. The clearest opportunity lies in creating space weather forecasting models that can respond in real time and that are built on both physics predictions and on observed data.

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“…The K-means clustering method is used to divide the data into k clusters. Moreover, it is used in recent studies like solar wind classification (Heidrich-Mesiner and Wimmer-Schweingruber, 2018) and short-term wind energy production forecast (Wang et al, 2018). In this method, random cluster centroids are assessed according to the k number which was determined in advance.…”

Section: Data Preprocessingmentioning

confidence: 99%

Short-term wind speed forecast for Urla wind power plant: A hybrid approach that couples weather research and forecasting model, weather patterns and SCADA data with comprehensive data preprocessing

Özen

Deniz

2022

Wind Engineering

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Short-term wind speed forecast model that uses both supervisory control and data acquisition (SCADA) based data and weather research and forecasting (WRF) model outputs for Urla wind power plant (WPP) has been proposed in this study. Two different WRF models were run to gather atmospheric variables from four surrounding grids of Urla WPP and calculate weather patterns affecting Urla WPP. After detecting outliers in the SCADA data by coupling of k-mean and isolation forest (IF) methods, statistical methods were used for data treatment and the outputs of WRF models were used for missing data imputation. The effect of each data type and data preprocessing techniques on the model was evaluated separately. The best model performance was achieved with 0.9085 [Formula: see text], and 0.81 MAE in the dataset which includes each data type and each data preprocessing was applied on. Otherwise, the dominant weather pattern affecting Urla WPP was found to be purely advective and the best result was achieved in this pattern.

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Solar Wind Classification Via k -Means Clustering Algorithm

Cited by 19 publications

References 31 publications

Systematic Analysis of Machine Learning and Feature Selection Techniques for Prediction of the Kp Index

Systematic Analysis of Machine Learning and Feature Selection Techniques for Prediction of the Kp Index

The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

Short-term wind speed forecast for Urla wind power plant: A hybrid approach that couples weather research and forecasting model, weather patterns and SCADA data with comprehensive data preprocessing

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